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Triangular Factorization Chapter 1 Triangular Factorization This chapter deals with the factorization of arbitrary matrices into products of triangular matrices. Since the solution of a linear n n system can be easily obtained once the matrix is factored into the product× of triangular matrices, we will concentrate on the factorization of square matrices. Specifically, we will show that an arbitrary n n matrix A has the factorization P A = LU where P is an n n permutation matrix,× L is an n n unit lower triangular matrix, and U is an n ×n upper triangular matrix. In connection× with this factorization we will discuss pivoting,× i.e., row interchange, strategies. We will also explore circumstances for which A may be factored in the forms A = LU or A = LLT . Our results for a square system will be given for a matrix with real elements but can easily be generalized for complex matrices. The corresponding results for a general m n matrix will be accumulated in Section 1.4. In the general case an arbitrary m× n matrix A has the factorization P A = LU where P is an m m permutation× matrix, L is an m m unit lower triangular matrix, and U is an×m n matrix having row echelon structure.× × 1.1 Permutation matrices and Gauss transformations We begin by defining permutation matrices and examining the effect of premulti- plying or postmultiplying a given matrix by such matrices. We then define Gauss transformations and show how they can be used to introduce zeros into a vector. Definition 1.1 An m m permutation matrix is a matrix whose columns con- sist of a rearrangement of× the m unit vectors e(j), j = 1,...,m, in RI m, i.e., a rearrangement of the columns (or rows) of the m m identity matrix. × The rows of an n n permutation matrix consist of a rearrangement of the T × transposes (e(j)) , j = 1,...,n, of the n unit vectors in RI n. The effect of premul- tiplying (postmultiplying) a matrix A by a permutation matrix P is to rearrange the rows (columns) of A into the same order as the rows (columns) of the identity matrix are ordered in P . For integers j and k such that 1 j k m, we denote ≤ ≤ ≤ by P(j,k) the special class of permutation matrices that result from the interchange 1 2 1. Triangular Factorization of the j-th and k-th columns of the identity matrix, i.e., (1.1) P = e(1), , e(j−1), e(k), e(j+1), , e(k−1), e(j), e(k+1), , e(m) . (j,k) · · · · · · · · · These permutation matrices, which are often referred to as elementary permutation matrices, have many useful properties such as T −1 (1.2) P(j,k) = P(j,k) = P(j,k) . Of course, P(j,j) = I. We now define a special class of matrices that are rank one perturbations of the identity matrix and that can be used to introduce zeros into a vector. Definition 1.2 For an integer j such that 1 j <m, let µ RI m such that µ = µ = = µ = 0. The m m matrix ≤ ∈ 1 2 · · · j × (1.3) M (j) = I µ(e(j))T − is known as a Gauss transformation. Here I denotes the m m identity matrix. Clearly M (j) is a unit lower triangular matrix, i.e., a lower× triangular matrix with 1’s on the diagonal. Also, off the main diagonal, M (j) has zero entries ev- erywhere except in the j-th column. The following proposition shows that Gauss transformations can be used to introduce zeros into a vector. Specifically, for a given nonzero x RI m, it shows how to choose an integer p and a µ RI m such that (1) ∈ ∈ M P(1,p)x has a nonzero entry only in its first component. T m Proposition 1.1 Given x = (x1, x2,...,xm) RI such that x = 0. Choose any integer p such that 1 p m and x =0. Define∈ µ RI m by 6 ≤ ≤ p 6 ∈ 0 if j =1 x1 if j = p and p =1 µj = x 6 p x j if j =2,...,m, j = p . xp 6 Then (1) (1) M P(1,p)x = xpe . Proof. The result follows from 0 xp µ2xp 0 (1) (1) T µ3xp 0 M P(1,p)x = P(1,p)x µ(e ) P(1,p)x = P(1,p)x = . − − . . . . µ x 0 m p 1.1. Permutation matrices and Gauss transformations 3 2 Note that if x = 0, then one may choose p = 1. Also, note that p, 1 p m, 1 6 ≤ ≤ can be any index such that xp = 0 so that there is not a unique Gauss transformation which transforms x into a constant6 times e(1); this is illustrated by the following example. Example 1.1 Let x= (0, 3, 4)T . If p = 2 and µ = (0, 0, 4/3)T , then − − 1 00 M (1) = 0 10 4/3 0 1 (1) T T and M P(1,2)x = ( 3, 0, 0) . On the other hand, if p = 3 and µ = (0, 3/4, 0) , then − − 1 00 M (1) = 3/4 1 0 0 01 (1) T and M P(1,3)x = (4, 0, 0) . We can also use Gauss transformations to introduce zeros in any contiguous block of a vector. For example, given x RI m and integers k and q, 1 k < q m, ∈ ≤ ≤ such that xj , j = k,...,q, are not all zero, suppose we want to choose a Gauss transformation that zeros out the (k + 1)-st through q-th components of P(k,p)x. Again, an integer p, k p q, is chosen so that x = 0. We set ≤ ≤ p 6 0 if j =1,...,k or j = q +1,...,m xk if j = p and p = k (1.4) µj = x 6 p x j if j = k +1,...,q, j = p x 6 p so that, from (1.3), Ik−1 1 µ − k+1 . . (1.5) M (k) = µq Im−k − 0 . . 0 4 1. Triangular Factorization and x1 . . x − k 1 xp 0 (k) M P(k,p)x = . . . 0 x q+1 . . x m We end this section by showing how an elementary permutation matrix which premultiplies a Gauss transformation “passes through” the Gauss transformation to produce another Gauss transformation postmultiplied by the same permutation matrix. Proposition 1.2 Let M (k) be a Gauss transformation and let j and p be indices such that k<j<p. Then there exists a Gauss transformation Mˆ (k) such that (k) ˆ (k) (1.6) P(j,p)M = M P(j,p) . Proof. The proof is by direct multiplication; Mˆ (k) is the matrix obtained by inter- changing the (j, k) and (p, k) entries of M (k). 2 1.2 Triangular factorizations of an n n matrix × The goal of this section is to prove that any n n matrix A has the factorization P A = LU where P is an n n permutation× matrix, L is an n n unit lower triangular matrix, and U is an ×n n upper triangular matrix. We will× first show how, through the use of elementary permutation× matrices and Gauss transformations, a given n n can be reduced to an upper triangular matrix U. We remark that the construction× given in the proof of the following proposition is exactly the classical Gaussian elimination algorithm expressed in matrix notation; we will examine this connection further in the next section. Proposition 1.3 Let A be a given n n matrix. Then there exist an integer ℓ, 0 ℓ n 1, Gauss transformation matrices× M (k), k = 1,...,ℓ, and elementary permutation≤ ≤ − matrices P , k =1,...,ℓ, k p n, such that (k,pk ) ≤ k ≤ (1.7) U = A(ℓ+1) = M (ℓ)P M (2)P M (1)P A (ℓ,pℓ) · · · (2,p2) (1,p1) is an n n upper triangular matrix. × 1.2. Triangular factorizations of an n × n matrix 5 (1) Proof. Starting with A = A and the integer counter σ1 = 1, we assume that at the start of the k-th stage, k 1, of the procedure we have a matrix of the form ≥ U (k) a(k) B(k) A(k) = , 0 c(k) D(k) (k) (k) k−1 (k) where U is an (k 1) (σk 1) upper triangular matrix,, a RI , c RI n−k+1, B(k) is (k −1) (×n σ −) and D(k) is (n k + 1) (n σ ).∈ If c(k) = 0, we∈ − × − k − × − k increment σk by one and move on to the next column, continuing to so increment (k) (k) σk until either c = 0 or σk >n. In the latter case we are finished since then A is upper triangular.6 In the former case we use (1.4) with a(k) x = c(k) (k) and q = m to choose an integer pk k and construct a Gauss transformation M (k) ≥ such that the components of M P(k,pk )x with indices j = k +1,...,m vanish. If we write M (k) in the block form I 0 k−1 , 0 M˜ (k) then M˜ (k) is an (m k + 1) (m k + 1) Gauss transformation formed by setting x = c(k) in Proposition− 1.1.× We have− that U (k) a(k) B(k) 0 c (k+1) (k) (k) 0 0 A = M P(k,pk )A = , .
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